The increasing availability of large scale data streams in recent years has led to efforts to track emerging topics. Emerging topics directed to politics, sports, world events, celebrity news, and other themes can appear in a variety of data sources including news feeds and social media. These emerging topics are sometimes known as “trends,” and current and popular emerging topics are said to be “trending.” Often, studies on emerging topics are centered on how new trends emerge, the longevity of trends, and the types of topics likely to trend.
Current efforts to track emerging topics generally include keyword-based searches. In this case, a search for emerging topics is conducted across data sources using known keywords. Generally, the occurrence of a keyword that is known to be associated with a topic is tallied across the various data sources, giving the researcher a general level of interest in the topic. These lists are generally compiled through the use of “supervised” algorithms that make inferences based on previously-identified keywords. However, keyword-based searches require the searcher to know the keywords and topics they are looking for, as well as requiring keywords to be linked to a specific topic. In particular, this type of search is not useful for newly created words or terms. Moreover, current efforts to track emerging topics are generally ineffective at identifying short-lived or newly emerging topics, and may not be able to track the evolution of topics over time.
Accordingly, improved methods and systems are needed and are disclosed herein that effectively identify trends and relationships between words in data.